Single cell transcriptomes of human blastoid cells
JD
2021-02-03
- BioProject Accession: PRJNA632846
- GEO Accession: GSE150570
Load required packages.
library(tidyverse)
library(magrittr)
library(Matrix)
library(patchwork)
library(extrafont)
# library(tidylog)Sys.time()## [1] "2021-02-03 20:59:43 CST"Data preparation
Functions loading
source(
file = file.path(
SCRIPT_DIR,
"utilities.R"
)
)
load_matrix <- function(x) {
matrix_readcount_use <- scipy.sparse$load_npz(
file.path(x, "matrix_readcount.npz")
)
colnames(matrix_readcount_use) <- np$load(
file.path(x, "matrix_readcount_barcodes.npy")
)
rownames(matrix_readcount_use) <- np$load(
file.path(x, "matrix_readcount_features.npy")
)
return(matrix_readcount_use)
}Data loading
PROJECT_DIR <- "/Users/jialei/Dropbox/Data/Projects/UTSW/Human_blastoid"Matrix
np <- reticulate::import("numpy", convert = TRUE)
scipy.sparse <- reticulate::import(module = "scipy.sparse", convert = TRUE)matrix_dir <- list(
"github/data/matrices/LW36",
"github/data/matrices/LW49_LW50_LW51_LW52",
"github/data/matrices/LW58_LW59",
"github/data/matrices/LW60_LW61",
"raw/public/PRJEB11202/reformatted_matrix"
)
matrix_readcount_use <- purrr::map(matrix_dir, function(x) {
load_matrix(file.path(PROJECT_DIR, x))
}) %>%
purrr::reduce(cbind)
walk(list(matrix_readcount_use, matrix_dir), function(x) {
print(object.size(x), units = "auto", standard = "SI")
})## 1.6 GB
## 808 B# clean up
rm(matrix_dir)Embedding
EMBEDDING_FILE <- "embedding.csv.gz"
embedding <- read_csv(
file = file.path(
PROJECT_DIR,
"github",
"data",
EMBEDDING_FILE
)
)
# clean up
rm(EMBEDDING_FILE)Metadata
cell_metadata_PRJEB11202 <- read_delim(
file.path(
PROJECT_DIR,
"raw/public/PRJEB11202/",
"E-MTAB-3929.sdrf.tsv"
),
delim = "\t"
) %>%
dplyr::select(
`Comment[ENA_SAMPLE]`,
`Comment[ENA_RUN]`,
`Characteristics[developmental stage]`,
`Characteristics[inferred lineage]`
) %>%
dplyr::rename(
cell = `Comment[ENA_SAMPLE]`,
run = `Comment[ENA_RUN]`,
developmental_stage = `Characteristics[developmental stage]`,
lineage = `Characteristics[inferred lineage]`
) %>%
mutate(
developmental_stage = str_replace(
string = developmental_stage,
pattern = "embryonic day ",
replacement = "E"
),
lineage = case_when(
lineage == "epiblast" ~ "EPI",
lineage == "primitive endoderm" ~ "HYP",
lineage == "trophectoderm" ~ "TE",
lineage == "not applicable" ~ "Pre-lineage"
)
)
embedding %<>%
left_join(
cell_metadata_PRJEB11202
) %>%
mutate(
lineage = case_when(
is.na(lineage) ~ "Blastoid",
TRUE ~ as.character(lineage)
),
developmental_stage = case_when(
is.na(developmental_stage) ~ "Blastoid",
TRUE ~ as.character(developmental_stage)
)
)matrix_readcount_use <- matrix_readcount_use[, embedding$cell]
matrix_cpm_use <- calc_cpm(matrix_readcount_use)
walk(list(matrix_readcount_use, matrix_cpm_use), function(x) {
print(object.size(x), units = "auto", standard = "SI")
})## 496.5 MB
## 496.5 MB# clean up
rm(cell_metadata_PRJEB11202)embedding %>%
mutate(
num_umis = colSums(matrix_readcount_use[, cell]),
num_genes = colSums(matrix_readcount_use[, cell] > 0)
) %>%
group_by(louvain) %>%
summarise(
num_cells = n(),
median_umis = median(num_umis),
median_genes = median(num_genes)
) %>%
gt::gt() %>%
gt::tab_options(table.font.size = "median")| louvain | num_cells | median_umis | median_genes |
|---|---|---|---|
| 0 | 1533 | 11534.0 | 3074.0 |
| 1 | 1113 | 18092.0 | 3612.0 |
| 2 | 1079 | 11308.0 | 2940.0 |
| 3 | 996 | 14659.0 | 3220.5 |
| 4 | 924 | 1108.5 | 627.0 |
| 5 | 725 | 6915.0 | 1981.0 |
| 6 | 710 | 9711.5 | 2158.0 |
| 7 | 672 | 10544.0 | 2791.5 |
| 8 | 649 | 11961.0 | 2905.0 |
| 9 | 570 | 19201.0 | 3656.0 |
| 10 | 410 | 23705.5 | 4208.5 |
| 11 | 366 | 13910.5 | 3355.5 |
| 12 | 365 | 16942.0 | 3437.0 |
| 13 | 275 | 8010.0 | 2455.0 |
| 14 | 249 | 31950.0 | 4954.0 |
| 15 | 202 | 8217.0 | 2452.0 |
| 16 | 138 | 10242.5 | 2852.5 |
| 17 | 120 | 17426.0 | 3400.0 |
| 18 | 86 | 18158.0 | 3853.5 |
embedding %>%
mutate(
num_umis = colSums(matrix_readcount_use[, cell]),
num_genes = colSums(matrix_readcount_use[, cell] > 0)
) %>%
group_by(batch) %>%
summarise(
num_cells = n(),
median_umis = median(num_umis),
median_genes = median(num_genes)
) %>%
gt::gt() %>%
gt::tab_options(table.font.size = "median")| batch | num_cells | median_umis | median_genes |
|---|---|---|---|
| LW60 | 4497 | 14421 | 3337 |
| LW61 | 5156 | 7625 | 2185 |
| PRJEB11202 | 1529 | 1551093 | 10305 |
Blastoids globally resemble blastocysts
Embedding visualization
EMBEDDING_TITLE_PREFIX <- "UMAP"
x_column <- "x_umap"
y_column <- "y_umap"Clustering & batch & UMI
p_embedding_batch <- plot_embedding(
embedding = embedding[, c(x_column, y_column)],
color_values = embedding$batch %>% as.factor(),
label = paste0(EMBEDDING_TITLE_PREFIX, "; Batch"),
label_position = NULL,
show_color_value_labels = FALSE,
show_color_legend = TRUE,
geom_point_size = 0.3,
sort_values = FALSE
) +
scale_color_manual(
values = gg_color_hue(n = length(unique(embedding$batch)))
) +
guides(colour = guide_legend(override.aes = list(size = 3), ncol = 1)) +
labs(color = NULL) +
customized_theme()
CB_POSITION <- c(0.875, 0.3)
p_embedding_umi <- plot_embedding_value(
embedding = embedding[, c(x_column, y_column)],
color_values = log10(
Matrix::colSums(matrix_readcount_use[, embedding$cell]) + 1
),
colorbar_position = CB_POSITION,
label = paste0(EMBEDDING_TITLE_PREFIX, "; UMI"),
label_position = NULL,
geom_point_size = 0.6,
sort_values = TRUE,
FUN = function(x) x
)
selected_feature <- "ENSG00000204531_POU5F1"
p_embedding_POU5F1 <- plot_embedding_value(
embedding = embedding[, c(x_column, y_column)],
color_values = log10(matrix_cpm_use[selected_feature, embedding$cell] + 1),
colorbar_position = CB_POSITION,
label = paste0(EMBEDDING_TITLE_PREFIX, "; ", selected_feature),
label_position = NULL,
geom_point_size = 0.6,
sort_values = TRUE,
FUN = function(x) x
)purrr::reduce(list(
p_embedding_batch,
p_embedding_umi,
p_embedding_POU5F1
), `+`) +
patchwork::plot_layout(ncol = 3) +
patchwork::plot_annotation(
theme = theme(plot.margin = margin())
)
Developmental stages
purrr::map(embedding %>%
tidyr::drop_na() %>%
pull(developmental_stage) %>%
unique() %>% sort(), function(x) {
plot_embedding(
embedding = embedding[, c(x_column, y_column)],
color_values = as.numeric(embedding$developmental_stage == x) %>% as.factor(),
label = paste0(EMBEDDING_TITLE_PREFIX, "; ", x),
label_position = NULL,
show_color_value_labels = FALSE,
show_color_legend = FALSE,
geom_point_size = 0.5,
sort_values = TRUE
) +
scale_color_manual(
values = c("grey70", "salmon")
)
}) %>%
purrr::reduce(`+`) +
plot_layout(ncol = 3) +
plot_annotation(
theme = theme(plot.margin = margin())
)
Polishing
color_palette_cluster <- c(
"0" = "#8DD3C7",
"1" = "#9EDAE5FF",
"2" = "#BEBADA",
"3" = "#FB8072",
"4" = "#80B1D3",
"5" = "#FDB462",
"6" = "#B3DE69",
"7" = "#FCCDE5",
"8" = "#DC863B",
"9" = "#BC80BD",
"10" = "#11c638",
"11" = "#BCBD22FF",
"12" = "#17BECFFF",
"13" = "#AEC7E8FF",
"14" = "#EAD3C6",
"15" = "#98DF8AFF",
"16" = "#FF9896FF",
"17" = "#C5B0D5FF",
"18" = "#C49C94FF",
"19" = "#F7B6D2FF",
"20" = "#D33F6A",
"21" = "#8E063B",
"22" = "#023FA5"
)
cluster_labels <- embedding %>%
dplyr::group_by(.data[["louvain"]]) %>%
dplyr::summarise(
x = quantile(.data[[x_column]], 0.5),
y = quantile(.data[[y_column]], 0.5),
.groups = "drop"
) %>%
as.data.frame()
cluster_labels[cluster_labels[["louvain"]] == 14, c("x", "y")] <- c(6.8, -1.8)
clusters_unidentified <- c(2, 3, 4, 5, 6, 7, 13, 15, 16)
cluster_labels %<>%
mutate(
label = case_when(
louvain %in% clusters_unidentified ~ paste0("U", as.character(louvain)),
TRUE ~ as.character(louvain)
)
)
# cluster
p_embedding_cluster <- embedding %>%
arrange(desc(louvain)) %>%
ggplot(
aes(
x = .data[[x_column]],
y = .data[[y_column]],
color = .data[["louvain"]] %>% as.factor()
)
) +
geom_point(
size = 0.45,
alpha = 1,
stroke = 0,
shape = 16
) +
scale_color_manual(
values = color_palette_cluster
) +
theme_void() +
ggplot2::guides(color = "none") +
ggplot2::annotate(
"text",
family = "Arial",
x = cluster_labels[, "x"],
y = cluster_labels[, "y"],
label = cluster_labels[, "label"],
parse = TRUE,
size = 2,
color = c("black")
)
# lineage
embedding_lineage_plotting <- embedding %>%
mutate(
lineage = factor(lineage,
levels = c("Blastoid", "TE", "HYP", "EPI", "Pre-lineage")
)
) %>%
arrange(lineage)
p_embedding_lineage <- plot_embedding(
embedding = embedding_lineage_plotting[, c(x_column, y_column)],
color_values = embedding_lineage_plotting$lineage %>% as.factor(),
label = NULL,
label_position = NULL,
show_color_value_labels = FALSE,
show_color_legend = TRUE,
geom_point_size = 0.4,
geom_point_alpha = 1,
sort_values = TRUE
) +
scale_color_manual(
name = NULL,
values = c("grey70", "#8ace7e", "#ff684c", "#9467bd", "#ffd8b1"),
breaks = c("Blastoid", "EPI", "HYP", "TE", "Pre-lineage"),
labels = c(
"Blastoid cells", "Epiblast (EPI)", "Hypoblast (HYP)",
"Trophectoderm (TE)", "Pre-lineage")
) +
customized_theme(legend_key_size = 1, legend_text_size = 5) +
guides(colour = guide_legend(override.aes = list(size = 2)))
# clean up
rm(embedding_lineage_plotting)
p_embedding_developmental_stage <- plot_embedding(
embedding = embedding[, c(x_column, y_column)],
color_values = embedding$developmental_stage %>% as.factor(),
label = NULL,
label_position = NULL,
show_color_value_labels = FALSE,
show_color_legend = TRUE,
geom_point_size = 0.45,
geom_point_alpha = 1,
sort_values = TRUE
) +
scale_color_manual(
name = NULL,
values = c(
"grey70",
ggthemes::tableau_color_pal("Tableau 10")(
length(unique(embedding$developmental_stage))
)
),
labels = c("Blastoid cells", "E3", "E4", "E5", "E6", "E7")
) +
customized_theme(legend_key_size = 1, legend_text_size = 5) +
guides(colour = guide_legend(override.aes = list(size = 2)))list(
p_embedding_cluster,
p_embedding_lineage,
p_embedding_developmental_stage
) %>%
purrr::reduce(`+`) +
patchwork::plot_layout(ncol = 1) +
patchwork::plot_annotation(
theme = theme(plot.margin = margin())
)CB_POSITION <- c(0.875, 0.32)
x_label <- ggplot_build(p_embedding_POU5F1)$layout$panel_params[[1]][c("x.range")] %>%
unlist() %>%
quantile(0.575)
y_label <- ggplot_build(p_embedding_POU5F1)$layout$panel_params[[1]][c("y.range")] %>%
unlist() %>%
quantile(0.8)
features_selected <- c(
"ENSG00000181449_SOX2",
"ENSG00000187498_COL4A1",
"ENSG00000179348_GATA2"
)
lineage_ <- c(
"(Epiblast)",
"(Hypoblast)",
"(Trophectoderm)"
)
p_embedding_SOX2_COL4A1_GATA2 <- purrr::map2(
features_selected,
lineage_, function(x, y) {
values <- log10(matrix_cpm_use[x, embedding$cell] + 1)
values[embedding$batch == "PRJEB11202"] <- NA
idx <- order(values, decreasing = FALSE, na.last = FALSE)
plot_embedding_value(
embedding = embedding[idx, c(x_column, y_column)],
color_values = values[idx],
colorbar_position = CB_POSITION,
label = str_c(x %>% str_remove("E.+_"), y, sep = "\n"),
label_position = c(x_label, y_label),
geom_point_size = 0.5,
sort_values = FALSE,
FUN = function(x) x,
label_size = 1.8
)
}
)
c(
list(
p_embedding_cluster,
p_embedding_lineage,
p_embedding_developmental_stage
),
p_embedding_SOX2_COL4A1_GATA2
) %>%
purrr::reduce(`+`) +
patchwork::plot_layout(ncol = 2, byrow = FALSE) +
patchwork::plot_annotation(
theme = theme(plot.margin = margin())
)
Expression
Embedding
features_selected <- c(
"ENSG00000204531_POU5F1",
"ENSG00000203909_DPPA5",
"ENSG00000184344_GDF3",
"ENSG00000156574_NODAL",
"ENSG00000111704_NANOG",
"ENSG00000075388_FGF4",
#
"ENSG00000164736_SOX17",
"ENSG00000125798_FOXA2",
"ENSG00000136574_GATA4",
"ENSG00000141448_GATA6",
"ENSG00000134853_PDGFRA",
"ENSG00000115414_FN1",
#
"ENSG00000107485_GATA3",
"ENSG00000118777_ABCG2",
"ENSG00000165556_CDX2",
"ENSG00000137869_CYP19A1",
"ENSG00000172818_OVOL1",
"ENSG00000126353_CCR7"
)
purrr::map(features_selected, function(x) {
values <- log10(matrix_cpm_use[x, embedding$cell] + 1)
values[embedding$batch == "PRJEB11202"] <- NA
idx <- order(values, decreasing = FALSE, na.last = FALSE)
plot_embedding_value(
embedding = embedding[idx, c(x_column, y_column)],
color_values = values[idx],
colorbar_position = CB_POSITION,
label = str_c(x %>% str_remove("E.+_"), sep = "\n"),
label_position = c(x_label, y_label),
geom_point_size = 0.5,
sort_values = FALSE,
FUN = function(x) x,
label_size = 1.8
)
}) %>%
purrr::reduce(`+`) +
patchwork::plot_layout(ncol = 3, byrow = FALSE) +
patchwork::plot_annotation(
theme = theme(plot.margin = margin())
)
Lollipop
clusters_selected_lollipop <- c(
11,
18,
0, 1, 8, 9, 12, 17
)
cells_selected_lollipop <- purrr::map(clusters_selected_lollipop, function(x) {
embedding %>%
filter(
louvain == x,
batch != "PRJEB11202"
) %>%
pull(cell)
})
names(cells_selected_lollipop) <- clusters_selected_lollipop
d <- c(
"ENSG00000181449_SOX2",
"ENSG00000156574_NODAL",
"ENSG00000111704_NANOG",
"ENSG00000147596_PRDM14",
"ENSG00000075388_FGF4",
#
"ENSG00000164736_SOX17",
"ENSG00000125798_FOXA2",
"ENSG00000136574_GATA4",
"ENSG00000141448_GATA6",
"ENSG00000115414_FN1",
"ENSG00000187498_COL4A1",
#
"ENSG00000179348_GATA2",
"ENSG00000107485_GATA3",
"ENSG00000118777_ABCG2",
#
"ENSG00000126353_CCR7",
"ENSG00000137869_CYP19A1",
"ENSG00000169550_MUC15",
"ENSG00000172818_OVOL1"
)
plot_lollipop(
cells = cells_selected_lollipop,
features = d,
matrix_cpm = matrix_cpm_use,
size_range_limits = c(0, 4),
dot_size = 3
) +
ggplot2::theme(
legend.box = "horizontal",
axis.text.x.top = element_text(angle = 90, vjust = 0.5, hjust = 0)
)
Violin
features_selected <- c(
"ENSG00000204531_POU5F1",
"ENSG00000203909_DPPA5",
"ENSG00000125798_FOXA2",
"ENSG00000134853_PDGFRA",
"ENSG00000107485_GATA3",
"ENSG00000118777_ABCG2"
)
clusters_selected_violin <- c(
11,
18,
0, 1, 8, 9, 12, 17
)
# blastoid
cells_violin <- purrr::map(clusters_selected_violin, function(x) {
embedding %>%
filter(
louvain == x,
batch != "PRJEB11202"
) %>%
pull(cell)
})
names(cells_violin) <- clusters_selected_violin
p_violin_blastoid <- plot_violin(
cells = cells_violin,
features = features_selected,
matrix_cpm = matrix_cpm_use,
x_range_breaks = NULL
) +
scale_fill_manual(
name = NULL,
values = color_palette_cluster
) +
scale_color_manual(
name = NULL,
values = color_palette_cluster
) +
labs(title = "Blastoid; 10x Genomics") +
theme(
strip.text = ggplot2::element_text(
family = "Arial",
size = 6
),
plot.title = element_text(
family = "Arial",
size = 8,
margin = ggplot2::margin(
t = 0, r = 0, b = 0, l = 0,
unit = "mm"
)
)
)
# in vivo
cells_violin <- purrr::map(clusters_selected_violin, function(x) {
embedding %>%
filter(
louvain == x,
batch == "PRJEB11202"
) %>%
pull(cell)
})
names(cells_violin) <- clusters_selected_violin
# re-aligned
p_violin_PRJEB11202 <- plot_violin(
cells = cells_violin,
features = features_selected,
matrix_cpm = matrix_cpm_use,
x_range_breaks = NULL,
y_title = NULL
) +
scale_fill_manual(
name = NULL,
values = color_palette_cluster
) +
scale_color_manual(
name = NULL,
values = color_palette_cluster
) +
labs(title = "Human pre-implantation embryos; Smart-Seq2") +
theme(
strip.text = ggplot2::element_text(
family = "Arial",
size = 6
),
plot.title = element_text(
family = "Arial",
size = 8,
margin = ggplot2::margin(
t = 0, r = 0, b = 0, l = 0,
unit = "mm"
)
)
)
p_violin_PRJEB11202 <- p_violin_PRJEB11202 +
theme(
axis.text.y = element_blank(),
axis.ticks.y = element_blank()
)
p_violin_blastoid_dims <- patchwork::get_dim(p_violin_blastoid)
p_violin_PRJEB11202_aligned <- patchwork::set_dim(
p_violin_PRJEB11202,
p_violin_blastoid_dims
)gridExtra::grid.arrange(
p_violin_blastoid,
p_violin_PRJEB11202_aligned,
ncol = 2,
clip = FALSE
)
Cluster composition
clusters_selected <- c(
11,
18,
0, 1, 8, 9, 12, 17,
10, 14
)
p_cell_distribution_lineage <- prepare_cluster_composition(
embedding = embedding,
x = louvain,
group = lineage
) %>%
filter(louvain %in% clusters_selected) %>%
mutate(
louvain = factor(louvain, levels = rev(clusters_selected)),
lineage = factor(lineage,
levels = rev(c("EPI", "HYP", "TE", "Pre-lineage", "Blastoid"))
)
) %>%
plot_barplot_cluster_composition(
x = percentage,
y = louvain,
z = lineage
) +
scale_fill_manual(
name = NULL,
values = c("grey85", "#8ace7e", "#ff684c", "#9467bd", "#ffd8b1"),
breaks = c("Blastoid", "EPI", "HYP", "TE", "Pre-lineage"),
labels = c(
"Blastoid cells", "Epiblast (EPI)", "Hypoblast (HYP)",
"Trophectoderm (TE)", "Pre-lineage"
)
)cell_distribution_labels_left <- tibble::tribble(
~louvain, ~lineage, ~label_position, ~percentage,
11L, "EPI", 0.068306011, 0.136612022,
18L, "HYP", 0.20930232, 0.37209302,
0L, "TE", 0.036855838, 0.063274625,
1L, "TE", 0.20350404, 0.33513028,
8L, "TE", 0.095531587, 0.181818182,
9L, "TE", 0.19649123, 0.2877193,
12L, "TE", 0.073972603, 0.126027397,
17L, "TE", 0.09583335, 0.1916667,
10L, "Pre-lineage", 0.284146342, 0.412195122,
14L, "Pre-lineage", 0.24698795, 0.4939759
) %>% mutate(
louvain = factor(louvain, levels = rev(clusters_selected))
)
cell_distribution_labels_right <- prepare_cluster_composition(
embedding = embedding,
x = louvain,
group = lineage
) %>%
filter(
lineage == "Blastoid",
louvain %in% clusters_selected
) %>%
mutate(
louvain = factor(
louvain,
levels = rev(louvain)
)
)
p_cell_distribution_lineage +
geom_text(
aes(
x = label_position,
y = louvain,
label = scales::percent(percentage, accuracy = 1L),
group = NULL
),
size = 1.8,
family = "Arial",
color = "black",
data = cell_distribution_labels_left,
hjust = 0.5
) +
geom_text(
aes(
x = 0.825,
y = louvain,
label = scales::percent(percentage, accuracy = 1L),
group = NULL
),
size = 1.8,
family = "Arial",
color = "black",
data = cell_distribution_labels_right,
hjust = 0
)
Heatmap construction
# prepare features; cells; heatmap matrix;
features_heatmap <- c(
"ENSG00000204531_POU5F1",
"ENSG00000184344_GDF3",
"ENSG00000203909_DPPA5",
"ENSG00000181449_SOX2",
"ENSG00000111704_NANOG",
"ENSG00000241186_TDGF1",
"ENSG00000156574_NODAL",
"ENSG00000254339_AC064802.1",
"ENSG00000283567_C19orf85",
"ENSG00000151650_VENTX",
"ENSG00000163032_VSNL1",
"ENSG00000159231_CBR3",
"ENSG00000145423_SFRP2",
"ENSG00000006468_ETV1",
"ENSG00000147596_PRDM14",
#
"ENSG00000136574_GATA4",
"ENSG00000164266_SPINK1",
"ENSG00000158966_CACHD1",
"ENSG00000164093_PITX2",
"ENSG00000087303_NID2",
"ENSG00000174358_SLC6A19",
"ENSG00000134962_KLB",
"ENSG00000167780_SOAT2",
"ENSG00000110245_APOC3",
"ENSG00000170558_CDH2",
"ENSG00000125848_FLRT3",
"ENSG00000129538_RNASE1",
"ENSG00000100079_LGALS2",
"ENSG00000134853_PDGFRA",
"ENSG00000164736_SOX17",
"ENSG00000017427_IGF1",
"ENSG00000275410_HNF1B",
"ENSG00000118137_APOA1",
"ENSG00000198336_MYL4",
"ENSG00000171557_FGG",
"ENSG00000125798_FOXA2",
"ENSG00000146374_RSPO3",
"ENSG00000115414_FN1",
"ENSG00000164292_RHOBTB3",
"ENSG00000141448_GATA6",
"ENSG00000187498_COL4A1",
#
"ENSG00000196549_MME",
"ENSG00000138814_PPP3CA",
"ENSG00000082074_FYB1",
"ENSG00000123191_ATP7B",
"ENSG00000175318_GRAMD2A",
"ENSG00000100593_ISM2",
"ENSG00000265107_GJA5",
"ENSG00000226887_ERVMER34-1",
"ENSG00000183734_ASCL2",
"ENSG00000143850_PLEKHA6",
"ENSG00000103534_TMC5",
"ENSG00000132470_ITGB4",
"ENSG00000180999_C1orf105",
"ENSG00000109610_SOD3",
"ENSG00000143369_ECM1",
"ENSG00000204632_HLA-G",
"ENSG00000113594_LIFR",
"ENSG00000168394_TAP1",
"ENSG00000183287_CCBE1",
"ENSG00000079393_DUSP13",
"ENSG00000121769_FABP3",
"ENSG00000182985_CADM1",
"ENSG00000164692_COL1A2",
"ENSG00000112655_PTK7",
"ENSG00000134873_CLDN10",
"ENSG00000108375_RNF43",
"ENSG00000254726_MEX3A",
"ENSG00000164120_HPGD",
"ENSG00000081026_MAGI3",
"ENSG00000120738_EGR1",
"ENSG00000108960_MMD",
"ENSG00000112137_PHACTR1",
"ENSG00000166450_PRTG",
"ENSG00000164099_PRSS12",
"ENSG00000143320_CRABP2",
"ENSG00000145681_HAPLN1",
"ENSG00000113196_HAND1",
"ENSG00000198300_PEG3",
"ENSG00000101144_BMP7",
"ENSG00000174498_IGDCC3",
"ENSG00000132005_RFX1",
"ENSG00000099814_CEP170B",
"ENSG00000101986_ABCD1",
"ENSG00000144648_ACKR2",
"ENSG00000074410_CA12",
"ENSG00000125740_FOSB",
"ENSG00000183979_NPB",
"ENSG00000214049_UCA1",
"ENSG00000153071_DAB2",
"ENSG00000126353_CCR7",
"ENSG00000105880_DLX5",
"ENSG00000124749_COL21A1",
"ENSG00000112041_TULP1",
"ENSG00000120211_INSL4",
"ENSG00000223850_MYCNUT",
"ENSG00000164707_SLC13A4",
"ENSG00000173157_ADAMTS20",
"ENSG00000196482_ESRRG",
"ENSG00000180875_GREM2",
"ENSG00000170255_MRGPRX1",
"ENSG00000267943_AC010328.1",
"ENSG00000172818_OVOL1",
"ENSG00000137270_GCM1",
"ENSG00000137869_CYP19A1",
"ENSG00000135678_CPM",
"ENSG00000171476_HOPX",
"ENSG00000249861_LGALS16",
"ENSG00000108244_KRT23",
"ENSG00000169550_MUC15",
"ENSG00000260034_LCMT1-AS2",
"ENSG00000269526_ERVV-1",
"ENSG00000028137_TNFRSF1B",
"ENSG00000124731_TREM1",
"ENSG00000072422_RHOBTB1",
"ENSG00000185215_TNFAIP2",
"ENSG00000164877_MICALL2",
"ENSG00000244476_ERVFRD-1",
"ENSG00000280109_PLAC4",
"ENSG00000111057_KRT18",
"ENSG00000170421_KRT8",
"ENSG00000169583_CLIC3",
"ENSG00000107485_GATA3",
"ENSG00000179348_GATA2",
"ENSG00000187186_AL162231.1",
"ENSG00000176155_CCDC57",
"ENSG00000133243_BTBD2",
"ENSG00000179364_PACS2",
"ENSG00000237651_C2orf74",
"ENSG00000125726_CD70",
"ENSG00000156587_UBE2L6",
"ENSG00000182165_TP53TG1",
"ENSG00000163017_ACTG2",
"ENSG00000143632_ACTA1",
"ENSG00000119632_IFI27L2",
"ENSG00000185847_LINC01405",
"ENSG00000116661_FBXO2",
"ENSG00000196878_LAMB3",
"ENSG00000122861_PLAU",
"ENSG00000142227_EMP3",
"ENSG00000011422_PLAUR",
"ENSG00000100985_MMP9",
"ENSG00000026508_CD44",
"ENSG00000104368_PLAT",
"ENSG00000164171_ITGA2",
"ENSG00000101198_NKAIN4",
"ENSG00000116774_OLFML3",
"ENSG00000125398_SOX9",
"ENSG00000124225_PMEPA1",
"ENSG00000118785_SPP1",
"ENSG00000163453_IGFBP7",
"ENSG00000182871_COL18A1",
"ENSG00000110925_CSRNP2",
"ENSG00000165886_UBTD1",
"ENSG00000115073_ACTR1B",
"ENSG00000256288_AC022075.3",
"ENSG00000228106_AL392172.1",
"ENSG00000163577_EIF5A2"
)
# heatmap matrix
clusters_selected_heatmap <- c(
11,
18,
0, 1, 8, 9, 12, 17
)
matrix_heatmap <- matrix_cpm_use[
features_heatmap,
embedding %>%
filter(
louvain %in% clusters_selected_heatmap,
batch != "PRJEB11202"
) %>%
pull(cell)
]
matrix_heatmap <- matrix_heatmap[rowSums(matrix_heatmap) != 0, ]
matrix_heatmap %>% dim()## [1] 158 3784matrix_heatmap <- log10(matrix_heatmap + 1)
matrix_heatmap <- t(scale(t(matrix_heatmap)))
heatmap_limits <- quantile(matrix_heatmap, c(0.05, 0.95))
matrix_heatmap[matrix_heatmap < heatmap_limits[1]] <- heatmap_limits[1]
matrix_heatmap[matrix_heatmap > heatmap_limits[2]] <- heatmap_limits[2]# sample cells
cells_heatmap_sampled <- purrr::map(clusters_selected_heatmap, function(x) {
cells_in_group <- embedding %>%
filter(
louvain == x,
batch != "PRJEB11202"
) %>%
pull(cell)
cat(length(cells_in_group), "\n")
if (length(cells_in_group) >= 100) {
c <- sample(cells_in_group, 100)
} else {
c <- cells_in_group
}
return(c)
})## 312
## 51
## 1428
## 682
## 528
## 376
## 310
## 97names(cells_heatmap_sampled) <- clusters_selected_heatmap
# cells
clusters_EPI <- 11
clusters_HYP <- 18
clusters_TE <- c(0, 1, 8, 9, 12, 17)
cells_heatmap_EPI <- embedding %>%
filter(
batch != "PRJEB11202",
louvain %in% clusters_EPI,
cell %in% unlist(cells_heatmap_sampled)
) %>%
pull(cell)
cells_heatmap_HYP <- embedding %>%
filter(
batch != "PRJEB11202",
louvain %in% clusters_HYP,
cell %in% unlist(cells_heatmap_sampled)
) %>%
pull(cell)
if (FALSE) {
cells_heatmap_TE <- embedding %>%
filter(
batch != "PRJEB11202",
louvain %in% clusters_TE,
cell %in% unlist(cells_heatmap_sampled)
) %>%
pull(cell)
}
cells_heatmap_TE <- cells_heatmap_sampled[as.character(clusters_TE)] %>%
unlist()
purrr::map_int(
list(cells_heatmap_EPI, cells_heatmap_HYP, cells_heatmap_TE),
length
)## [1] 100 51 597# column annotation
ha_cluster <- embedding[
match(
x = cells_heatmap_sampled %>% unlist(),
table = embedding$cell
), "louvain",
drop = TRUE
]
ha_lineage <- rep("TE", length(ha_cluster))
ha_lineage[ha_cluster == "11"] <- "EPI"
ha_lineage[ha_cluster %in% c("18")] <- "HYP"# map color
col_fun <- circlize::colorRamp2(
quantile(
c(
min(matrix_heatmap),
max(matrix_heatmap)
),
seq(0, 1, 0.1)
),
viridis::plasma(11)
)# features to mark
features_to_mark_left <- c(
"ENSG00000184344_GDF3",
"ENSG00000241186_TDGF1",
"ENSG00000156574_NODAL",
"ENSG00000066468_FGFR2",
"ENSG00000111704_NANOG",
"ENSG00000204531_POU5F1",
"ENSG00000203909_DPPA5",
"ENSG00000181449_SOX2",
"ENSG00000186103_ARGFX",
"ENSG00000147596_PRDM14",
"ENSG00000171872_KLF17",
"ENSG00000086548_CEACAM6",
"ENSG00000166073_GPR176",
"ENSG00000125740_FOSB",
"ENSG00000105880_DLX5",
"ENSG00000169583_CLIC3",
"ENSG00000244476_ERVFRD-1",
"ENSG00000072422_RHOBTB1",
"ENSG00000137869_CYP19A1",
"ENSG00000169550_MUC15",
"ENSG00000108244_KRT23",
"ENSG00000260034_LCMT1-AS2"
)
features_to_mark_right <- c(
"ENSG00000187498_COL4A1",
"ENSG00000136574_GATA4",
"ENSG00000115414_FN1",
"ENSG00000125798_FOXA2",
"ENSG00000164736_SOX17",
"ENSG00000141448_GATA6",
"ENSG00000275410_HNF1B",
"ENSG00000153707_PTPRD",
"ENSG00000101441_CST4",
"ENSG00000146374_RSPO3",
"ENSG00000111057_KRT18",
"ENSG00000170421_KRT8",
"ENSG00000153071_DAB2",
"ENSG00000107485_GATA3",
"ENSG00000179348_GATA2",
"ENSG00000144648_ACKR2",
"ENSG00000126353_CCR7",
"ENSG00000137270_GCM1",
"ENSG00000180875_GREM2",
"ENSG00000172818_OVOL1",
"ENSG00000265107_GJA5",
"ENSG00000132470_ITGB4",
"ENSG00000074410_CA12",
"ENSG00000124749_COL21A1"
)
#
features_to_mark_left <- features_to_mark_left[
features_to_mark_left %in% rownames(matrix_heatmap)
]
ha_left <- ComplexHeatmap::rowAnnotation(
foo = ComplexHeatmap::anno_mark(
at = which(rownames(matrix_heatmap) %in% features_to_mark_left),
labels = features_to_mark_left %>% str_remove(pattern = "E.+_"),
which = "row",
side = "left",
lines_gp = grid::gpar(col = "grey50"),
labels_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
)
)
)
#
features_to_mark_right <- features_to_mark_right[
features_to_mark_right %in% rownames(matrix_heatmap)
]
ha_right <- ComplexHeatmap::rowAnnotation(
foo = ComplexHeatmap::anno_mark(
at = which(rownames(matrix_heatmap) %in% features_to_mark_right),
labels = features_to_mark_right %>% str_remove(pattern = "E.+_"),
which = "row",
side = "right",
lines_gp = grid::gpar(col = "grey50"),
labels_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
)
)
)use_raster <- FALSE# EPI
anno_labels_tbl_EPI <- table(
ha_cluster[ha_cluster %in% clusters_EPI]
)[as.character(clusters_EPI)] %>%
as_tibble() %>%
mutate(
cum_sum = cumsum(value),
position = cum_sum - value / 2
)
anno_labels_cluster_EPI <- rep(NA, length(ha_cluster[ha_cluster %in% clusters_EPI]))
if (nchar(as.character(clusters_EPI)) > 1) {
cluster_label <- strsplit(as.character(clusters_EPI), "")[[1]]
anno_labels_cluster_EPI[anno_labels_tbl_EPI$position - 5] <- cluster_label[1]
anno_labels_cluster_EPI[anno_labels_tbl_EPI$position + 5] <- cluster_label[2]
} else {
anno_labels_cluster_EPI[anno_labels_tbl_EPI$position] <- as.character(clusters_EPI)
}
# anno_labels_cluster_EPI %>% table(exclude = "")
ha_column_EPI <- ComplexHeatmap::HeatmapAnnotation(
#
lineage = ComplexHeatmap::anno_simple(
ha_lineage[ha_lineage == "EPI"],
# pch = anno_labels_cluster,
col = setNames(
object = c("grey85", "#8ace7e", "#ff684c", "#9467bd", "#ffd8b1"),
nm = c("Blastoid", "EPI", "HYP", "TE", "n/a")
),
which = "column",
pt_size = unit(2, "mm"),
pt_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
),
simple_anno_size = unit(2, "mm")
),
#
cluster = ComplexHeatmap::anno_simple(
ha_cluster[ha_cluster %in% clusters_EPI],
pch = anno_labels_cluster_EPI,
col = color_palette_cluster[as.character(clusters_selected_heatmap)],
which = "column",
# pt_size = unit(2, "mm"),
pt_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
),
simple_anno_size = unit(2, "mm")
),
#
show_annotation_name = TRUE,
annotation_label = c(
"Lineage",
"Cluster"
),
annotation_name_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
annotation_name_side = "left"
)
ht_EPI <- ComplexHeatmap::Heatmap(
matrix = matrix_heatmap[, cells_heatmap_EPI] %>% as.matrix(),
rect_gp = grid::gpar(col = NA, lwd = 0),
# col = viridis::plasma(n = 10),
# col = wesanderson::wes_palette("Zissou1", 50, type = "continuous"),
col = col_fun,
#
# row_title = character(0),
# row_title_side = c("left", "right"),
row_title_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
row_title_rot = 0,
# column_title = character(0),
# column_title_side = c("top", "bottom"),
column_title_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
column_title_rot = 0,
#
cluster_rows = FALSE,
show_row_dend = FALSE,
cluster_columns = FALSE,
show_column_dend = FALSE,
#
show_row_names = FALSE,
# row_labels = str_remove(string = rownames(matrix_heatmap), pattern = "^E.+_"),
# row_names_side = c("left"),
# row_names_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
# row_names_rot = 0,
# row_names_centered = FALSE,
show_column_names = FALSE,
# column_labels = colnames(matrix),
# column_names_side = c("top"),
# column_names_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
# column_names_rot = 90,
# column_names_centered = FALSE,
#
top_annotation = ha_column_EPI,
bottom_annotation = NULL,
left_annotation = ha_left,
right_annotation = NULL,
#
# row_split = table_s2_sheet4$ClusterID,
# row_gap = unit(0.3, "mm"),
column_split = factor(
ha_lineage[ha_lineage == "EPI"],
levels = unique(ha_lineage[ha_lineage == "EPI"])
),
column_gap = unit(0, "mm"),
#
show_heatmap_legend = FALSE,
heatmap_legend_param = list(
# title = expression(paste("Log"[10], " (CPM + 1)")),
title = "Expr",
title_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 6
),
legend_direction = "vertical",
labels_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
),
legend_height = unit(15, "mm"),
legend_width = unit(5, "mm")
),
#
use_raster = use_raster,
#
width = unit(8, "mm")
)
# ComplexHeatmap::draw(ht_EPI)# HYP
anno_labels_tbl_HYP <- table(
ha_cluster[ha_cluster %in% clusters_HYP]
)[as.character(clusters_HYP)] %>%
as_tibble() %>%
mutate(
cum_sum = cumsum(value),
position = cum_sum - value / 2
)
anno_labels_cluster_HYP <- rep(NA, length(ha_cluster[ha_cluster %in% clusters_HYP]))
# anno_labels_cluster_HYP[anno_labels_tbl_HYP$position] <- as.character(clusters_HYP)
# anno_labels_cluster_HYP %>% table(exclude = "")
if (nchar(as.character(clusters_HYP)) > 1) {
cluster_label <- strsplit(as.character(clusters_HYP), "")[[1]]
anno_labels_cluster_HYP[anno_labels_tbl_HYP$position - 5] <- cluster_label[1]
anno_labels_cluster_HYP[anno_labels_tbl_HYP$position + 5] <- cluster_label[2]
} else {
anno_labels_cluster_HYP[anno_labels_tbl_HYP$position] <- as.character(clusters_HYP)
}
ha_column_HYP <- ComplexHeatmap::HeatmapAnnotation(
#
lineage = ComplexHeatmap::anno_simple(
ha_lineage[ha_lineage == "HYP"],
# pch = anno_labels_cluster,
col = setNames(
object = c("grey85", "#8ace7e", "#ff684c", "#9467bd", "#ffd8b1"),
nm = c("Blastoid", "EPI", "HYP", "TE", "n/a")
),
which = "column",
pt_size = unit(2, "mm"),
pt_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
),
simple_anno_size = unit(2, "mm")
),
#
cluster = ComplexHeatmap::anno_simple(
ha_cluster[ha_cluster %in% clusters_HYP],
pch = anno_labels_cluster_HYP,
col = color_palette_cluster[as.character(clusters_selected_heatmap)],
which = "column",
# pt_size = unit(2, "mm"),
pt_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
),
simple_anno_size = unit(2, "mm")
),
#
show_annotation_name = FALSE,
annotation_label = c(
"Lineage",
"Cluster"
),
annotation_name_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
annotation_name_side = "left"
)
ht_HYP <- ComplexHeatmap::Heatmap(
matrix = matrix_heatmap[, cells_heatmap_HYP] %>% as.matrix(),
rect_gp = grid::gpar(col = NA, lwd = 0),
# col = viridis::plasma(n = 10),
# col = wesanderson::wes_palette("Zissou1", 50, type = "continuous"),
col = col_fun,
#
# row_title = character(0),
# row_title_side = c("left", "right"),
row_title_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
row_title_rot = 0,
# column_title = character(0),
# column_title_side = c("top", "bottom"),
column_title_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
column_title_rot = 0,
#
cluster_rows = FALSE,
show_row_dend = FALSE,
cluster_columns = FALSE,
show_column_dend = FALSE,
#
show_row_names = FALSE,
# row_labels = str_remove(string = rownames(matrix_heatmap), pattern = "^E.+_"),
# row_names_side = c("left"),
# row_names_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
# row_names_rot = 0,
# row_names_centered = FALSE,
show_column_names = FALSE,
# column_labels = colnames(matrix),
# column_names_side = c("top"),
# column_names_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
# column_names_rot = 90,
# column_names_centered = FALSE,
#
top_annotation = ha_column_HYP,
bottom_annotation = NULL,
left_annotation = NULL,
right_annotation = NULL,
#
# row_split = table_s2_sheet4$ClusterID,
# row_gap = unit(0.3, "mm"),
column_split = factor(
ha_lineage[ha_lineage == "HYP"],
levels = unique(ha_lineage[ha_lineage == "HYP"])
),
column_gap = unit(0, "mm"),
#
show_heatmap_legend = FALSE,
heatmap_legend_param = list(
# title = expression(paste("Log"[10], " (CPM + 1)")),
title = "Expr",
title_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 6
),
legend_direction = "vertical",
labels_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
),
legend_height = unit(15, "mm"),
legend_width = unit(5, "mm")
),
#
use_raster = use_raster,
#
width = unit(5, "mm")
)
# ComplexHeatmap::draw(ht_HYP)# TE
anno_labels_tbl_TE <- table(
ha_cluster[ha_cluster %in% clusters_TE]
)[as.character(clusters_TE)] %>%
enframe() %>%
mutate(
cum_sum = cumsum(value),
position = cum_sum - value / 2
)
anno_labels_tbl_TE <- purrr::map(anno_labels_tbl_TE$name, function(x) {
# works but ugly
a <- anno_labels_tbl_TE %>%
filter(name == x)
if (nchar(a$name) > 1) {
cluster_label <- strsplit(as.character(a$name), "")[[1]]
a <- rbind(a, a)
a[1, 1] <- cluster_label[1]
a[2, 1] <- cluster_label[2]
a %<>% as.data.frame()
a[1, 4] <- a[1, 4] - 5
a[2, 4] <- a[2, 4] + 5
}
return(a)
}) %>%
purrr::reduce(rbind)
anno_labels_cluster_TE <- rep(NA, length(ha_cluster[ha_cluster %in% clusters_TE]))
anno_labels_cluster_TE[anno_labels_tbl_TE$position] <- anno_labels_tbl_TE$name
# anno_labels_cluster_TE %>% table(exclude = "")
ha_column_TE <- ComplexHeatmap::HeatmapAnnotation(
#
lineage = ComplexHeatmap::anno_simple(
ha_lineage[ha_lineage == "TE"],
# pch = anno_labels_cluster,
col = setNames(
object = c("grey85", "#8ace7e", "#ff684c", "#9467bd", "#ffd8b1"),
nm = c("Blastoid", "EPI", "HYP", "TE", "n/a")
),
which = "column",
pt_size = unit(2, "mm"),
pt_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
),
simple_anno_size = unit(2, "mm")
),
#
cluster = ComplexHeatmap::anno_simple(
ha_cluster[ha_cluster %in% clusters_TE],
pch = anno_labels_cluster_TE,
col = color_palette_cluster[as.character(clusters_selected_heatmap)],
which = "column",
# pt_size = unit(2, "mm"),
pt_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
),
simple_anno_size = unit(2, "mm")
),
#
show_annotation_name = FALSE,
annotation_label = c(
"Lineage",
"Cluster"
),
annotation_name_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
annotation_name_side = "left"
)
ht_TE <- ComplexHeatmap::Heatmap(
# matrix = matrix_heatmap[, colnames(matrix_heatmap) %in% cells_heatmap_TE] %>% as.matrix(),
matrix = matrix_heatmap[, cells_heatmap_TE] %>% as.matrix(),
rect_gp = grid::gpar(col = NA, lwd = 0),
# col = viridis::plasma(n = 10),
# col = wesanderson::wes_palette("Zissou1", 50, type = "continuous"),
col = col_fun,
#
# row_title = character(0),
# row_title_side = c("left", "right"),
row_title_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
row_title_rot = 0,
# column_title = character(0),
# column_title_side = c("top", "bottom"),
column_title_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
column_title_rot = 0,
#
cluster_rows = FALSE,
show_row_dend = FALSE,
cluster_columns = FALSE,
show_column_dend = FALSE,
#
show_row_names = FALSE,
# row_labels = str_remove(string = rownames(matrix_heatmap), pattern = "^E.+_"),
# row_names_side = c("left"),
# row_names_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
# row_names_rot = 0,
# row_names_centered = FALSE,
show_column_names = FALSE,
# column_labels = colnames(matrix),
# column_names_side = c("top"),
# column_names_gp = grid::gpar(fontfamily = "Arial", fontsize = 6),
# column_names_rot = 90,
# column_names_centered = FALSE,
#
top_annotation = ha_column_TE,
bottom_annotation = NULL,
left_annotation = NULL,
right_annotation = ha_right,
#
# row_split = table_s2_sheet4$ClusterID,
# row_gap = unit(0.3, "mm"),
column_split = factor(
ha_lineage[ha_lineage == "TE"],
levels = unique(ha_lineage[ha_lineage == "TE"])
),
column_gap = unit(0, "mm"),
#
show_heatmap_legend = FALSE,
heatmap_legend_param = list(
# title = expression(paste("Log"[10], " (CPM + 1)")),
title = "Expr",
title_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 6
),
legend_direction = "vertical",
labels_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
),
legend_height = unit(15, "mm"),
legend_width = unit(5, "mm")
),
#
use_raster = use_raster,
#
width = unit(50, "mm")
)# legend
lgd_colorbar <- ComplexHeatmap::Legend(
col_fun = col_fun,
title = "Expr",
grid_height = unit(1, "mm"),
grid_width = unit(2, "mm"),
legend_height = unit(10, "mm"),
legend_width = unit(2, "mm"),
title_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 6
),
labels_gp = grid::gpar(
fontfamily = "Arial",
fontsize = 5
)
)
pd <- ComplexHeatmap::packLegend(
# lgd_cluster,
# lgd_lineage,
lgd_colorbar,
# gap = unit(8, "mm"),
direction = "vertical"
)ComplexHeatmap::draw(
ht_EPI + ht_HYP + ht_TE,
heatmap_legend_list = list(pd),
ht_gap = unit(0, "mm")
)
Gene Ontology enrichment
enriched_go <- tibble::tribble(
~category, ~rank, ~go_id, ~term, ~p_value, ~p_value_log,
"EPI", 1L, "GO:0019827", "stem cell population maintenance", 9e-07, 6.045757491,
"EPI", 2L, "GO:0048368", "lateral mesoderm development", 0.00053, 3.27572413,
"EPI", 3L, "GO:0009790", "embryo development", 0.00088, 3.055517328,
"HYP", 1L, "GO:0048598", "embryonic morphogenesis", 3.9e-07, 6.408935393,
"HYP", 2L, "GO:0007369", "gastrulation", 1.8e-05, 4.744727495,
"HYP", 3L, "GO:0007492", "endoderm development", 0.00284, 2.54668166,
"TE", 1L, "GO:0001890", "placenta development", 6e-11, 10.22184875,
"TE", 2L, "GO:0001892", "embryonic placenta development", 2e-08, 7.698970004,
"TE", 3L, "GO:0048513", "animal organ development", 2.7e-07, 6.568636236
)
enriched_go %>%
mutate(
category = factor(
category,
levels = c("EPI", "HYP", "TE") # %>% rev()
),
rank = factor(
rank,
levels = c(3, 2, 1)
)
) %>%
plot_barplot_go_enrichment(
x = p_value_log,
y = rank,
z = category
) +
facet_wrap(
~category,
nrow = 1,
strip.position = "left",
scales = "free_x",
labeller = labeller(
category = c("EPI" = "EPI", "HYP" = "HYP", "TE" = "TE")
)
) +
geom_text(
aes(
x = 0,
label = term,
group = NULL
),
size = 2.2,
family = "Arial",
color = "black",
data = enriched_go %>%
mutate(
category = factor(
category,
levels = c("EPI", "HYP", "TE")
),
rank = factor(
rank,
levels = c(3, 2, 1)
),
term = paste(" ", term)
),
hjust = 0
) +
scale_fill_manual(
values = c("#8ace7e", "#ff684c", "#9467bd")
)
High concordance of blastoid replicates
Data loading
EMBEDDING_FILE <- "embedding_replicate.csv.gz"
embedding_replicate <- read_csv(
file = file.path(
PROJECT_DIR,
"github",
"data",
EMBEDDING_FILE
)
) %>%
dplyr::select(cell:y_fitsne)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## cell = col_character(),
## batch = col_character(),
## louvain = col_double(),
## x_tsne = col_double(),
## y_tsne = col_double(),
## x_umap = col_double(),
## y_umap = col_double(),
## x_fitsne = col_double(),
## y_fitsne = col_double(),
## x_phate = col_double(),
## y_phate = col_double(),
## `x_min_dist=0.1` = col_double(),
## `y_min_dist=0.1` = col_double(),
## x_multicoretsne = col_double(),
## y_multicoretsne = col_double()
## )rm(EMBEDDING_FILE)Embedding visualization
EMBEDDING_TITLE_PREFIX <- "FIt-SNE"
x_column <- "x_fitsne"
y_column <- "y_fitsne"Clustering & batch & UMI
p_embedding_replicate_cluster <- plot_embedding(
embedding = embedding_replicate[, c(x_column, y_column)],
color_values = embedding_replicate$louvain %>% as.factor(),
label = paste0(EMBEDDING_TITLE_PREFIX, "; Cluster"),
label_position = NULL,
show_color_value_labels = TRUE,
show_color_legend = FALSE,
geom_point_size = 0.6,
sort_values = FALSE
) +
scale_color_manual(
values = gg_color_hue(n = length(unique(embedding_replicate$louvain)))
)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.p_embedding_replicate_batch <- plot_embedding(
embedding = embedding_replicate[, c(x_column, y_column)],
color_values = embedding_replicate$batch %>% as.factor(),
label = paste0(EMBEDDING_TITLE_PREFIX, "; Batch"),
label_position = NULL,
show_color_value_labels = FALSE,
show_color_legend = TRUE,
geom_point_size = 0.3,
sort_values = FALSE
) +
scale_color_manual(
values = gg_color_hue(n = length(unique(embedding_replicate$batch)))
) +
guides(colour = guide_legend(override.aes = list(size = 3), ncol = 1)) +
labs(color = NULL) +
customized_theme()## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.CB_POSITION <- c(0.875, 0.3)
p_embedding_replicate_umi <- plot_embedding_value(
embedding = embedding_replicate[, c(x_column, y_column)],
color_values = log10(Matrix::colSums(matrix_readcount_use[, embedding_replicate$cell]) + 1),
colorbar_position = CB_POSITION,
label = paste0(EMBEDDING_TITLE_PREFIX, "; UMI"),
label_position = NULL,
geom_point_size = 0.6,
sort_values = TRUE,
FUN = function(x) x
)purrr::reduce(
list(
p_embedding_replicate_cluster,
p_embedding_replicate_batch,
p_embedding_replicate_umi
), `+`
) +
patchwork::plot_layout(ncol = 3) +
patchwork::plot_annotation(
theme = theme(plot.margin = margin())
)
Cluster composition
prepare_cluster_composition(
embedding = embedding_replicate,
x = louvain,
group = batch
) %>%
mutate(
louvain = as.factor(louvain)
) %>%
plot_barplot(
x = louvain,
y = percentage,
z = batch
)
Blastoids derived stem cell lines
Data loading
EMBEDDING_FILE <- "embedding_stem.csv.gz"
embedding_stem <- read_csv(
file = file.path(
PROJECT_DIR,
"github",
"data",
EMBEDDING_FILE
)
)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## cell = col_character(),
## batch = col_character(),
## louvain = col_double(),
## x_tsne = col_double(),
## y_tsne = col_double(),
## x_umap = col_double(),
## y_umap = col_double(),
## x_fitsne = col_double(),
## y_fitsne = col_double(),
## x_phate = col_double(),
## y_phate = col_double(),
## `x_min_dist=0.1` = col_double(),
## `y_min_dist=0.1` = col_double(),
## x_multicoretsne = col_double(),
## y_multicoretsne = col_double()
## )rm(EMBEDDING_FILE)Embedding visualization
EMBEDDING_TITLE_PREFIX <- "FIt-SNE"
x_column <- "x_fitsne"
y_column <- "y_fitsne"Clustering & batch & UMI
p_embedding_stem_batch <- plot_embedding(
embedding = embedding_stem[, c(x_column, y_column)],
color_values = embedding_stem$batch %>% as.factor(),
label = paste0(EMBEDDING_TITLE_PREFIX, "; Batch"),
label_position = NULL,
show_color_value_labels = FALSE,
show_color_legend = TRUE,
geom_point_size = 0.6,
sort_values = FALSE
) +
scale_color_manual(
values = gg_color_hue(n = length(unique(embedding_stem$batch)))
) +
guides(colour = guide_legend(override.aes = list(size = 3), ncol = 1)) +
labs(color = NULL) +
customized_theme()
CB_POSITION <- c(0.875, 0.3)
p_embedding_stem_umi <- plot_embedding_value(
embedding = embedding_stem[, c(x_column, y_column)],
color_values = log10(Matrix::colSums(matrix_readcount_use[, embedding_stem$cell]) + 1),
colorbar_position = CB_POSITION,
label = paste0(EMBEDDING_TITLE_PREFIX, "; UMI"),
label_position = NULL,
geom_point_size = 0.6,
sort_values = TRUE,
FUN = function(x) x
)
selected_feature <- "ENSG00000204531_POU5F1"
p_embedding_stem_POU5F1 <- plot_embedding_value(
embedding = embedding_stem[, c(x_column, y_column)],
color_values = log10(matrix_cpm_use[selected_feature, embedding_stem$cell] + 1),
colorbar_position = CB_POSITION,
label = paste0(EMBEDDING_TITLE_PREFIX, "; ", selected_feature),
label_position = NULL,
geom_point_size = 0.6,
sort_values = TRUE,
FUN = function(x) x
)purrr::reduce(list(
p_embedding_stem_batch,
p_embedding_stem_umi,
p_embedding_stem_POU5F1
), `+`) +
patchwork::plot_layout(ncol = 3) +
patchwork::plot_annotation(
theme = theme(plot.margin = margin())
)
Polishing
p_embedding_stem_batch <- plot_embedding(
embedding = embedding_stem[, c(x_column, y_column)],
color_values = embedding_stem$batch %>% as.factor(),
# label = "t-SNE; Batch",
label_position = NULL,
show_color_value_labels = FALSE,
show_color_legend = TRUE,
geom_point_size = 0.5,
sort_values = FALSE
) +
scale_color_manual(
values = gg_color_hue(n = length(unique(embedding_stem$batch))),
labels = c(
"Naïve ESCs; 5iLA",
"Blastoid derived nESCs; 5iLA",
"Blastoid derived nEND; NACL",
"Blastoid derived hTSCs; hTSM"
)
) +
guides(colour = guide_legend(override.aes = list(size = 2), ncol = 1)) +
labs(color = NULL) +
customized_theme(legend_key_size = 1, legend_text_size = 5) +
guides(
colour = guide_legend(
ncol = 1,
override.aes = list(size = 2)
),
byrow = TRUE
)## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.x_label <- ggplot_build(p_embedding_stem_batch)$layout$panel_params[[1]][c("x.range")] %>%
unlist() %>%
quantile(0.1)
y_label <- ggplot_build(p_embedding_stem_batch)$layout$panel_params[[1]][c("y.range")] %>%
unlist() %>%
quantile(0.8)
features_selected <- c(
"ENSG00000181449_SOX2",
"ENSG00000141448_GATA6",
"ENSG00000107485_GATA3"
)
p_embedding_stem_SOX2_GATA6_GATA3 <- purrr::map(
features_selected, function(x) {
CB_POSITION <- c(0.875, 0.35)
values <- log10(matrix_cpm_use[x, embedding_stem$cell] + 1)
plot_embedding_value(
embedding = embedding_stem[, c(x_column, y_column)],
color_values = values,
colorbar_position = CB_POSITION,
label = str_c(x %>% str_remove("E.+_"), sep = "\n"),
label_position = c(x_label, y_label),
geom_point_size = 0.5,
sort_values = TRUE,
FUN = function(x) x,
label_size = 1.8
)
}
)# compose
list(
p_embedding_stem_batch,
p_embedding_stem_SOX2_GATA6_GATA3
) %>%
purrr::reduce(`+`) +
patchwork::plot_layout(nrow = 2) +
patchwork::plot_annotation(
theme = theme(plot.margin = margin())
)
Trajectory inference
Data loading
EMBEDDING_FILE <- "embedding_timecourse.csv.gz"
embedding_timecourse <- read_csv(
file = file.path(
PROJECT_DIR,
"github",
"data",
EMBEDDING_FILE
)
)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## cell = col_character(),
## batch = col_character(),
## louvain = col_double(),
## x_tsne = col_double(),
## y_tsne = col_double(),
## x_umap = col_double(),
## y_umap = col_double(),
## x_fitsne = col_double(),
## y_fitsne = col_double(),
## x_phate = col_double(),
## y_phate = col_double(),
## `x_min_dist=0.1` = col_double(),
## `y_min_dist=0.1` = col_double(),
## x_multicoretsne = col_double(),
## y_multicoretsne = col_double()
## )rm(EMBEDDING_FILE)Embedding visualization
EMBEDDING_TITLE_PREFIX <- "FIt-SNE"
x_column <- "x_fitsne"
y_column <- "y_fitsne"Clustering & batch & UMI
p_embedding_timecourse_cluster <- plot_embedding(
embedding = embedding_timecourse[, c(x_column, y_column)],
color_values = letters[as.integer(embedding_timecourse$louvain + 1)] %>% as.factor(),
label = paste0(EMBEDDING_TITLE_PREFIX, ";", " Cluster"),
label_position = NULL,
show_color_value_labels = TRUE,
show_color_legend = FALSE,
geom_point_size = 0.6,
sort_values = FALSE
) +
scale_color_manual(
values = gg_color_hue(n = length(unique(embedding_timecourse$louvain)))
)
p_embedding_timecourse_batch <- plot_embedding(
embedding = embedding_timecourse[, c(x_column, y_column)],
color_values = embedding_timecourse$batch %>% as.factor(),
label = paste0(EMBEDDING_TITLE_PREFIX, "; Batch"),
label_position = NULL,
show_color_value_labels = FALSE,
show_color_legend = TRUE,
geom_point_size = 0.45,
sort_values = FALSE
) +
scale_color_manual(
values = gg_color_hue(n = length(unique(embedding_timecourse$batch)))
) +
guides(colour = guide_legend(override.aes = list(size = 3), ncol = 1)) +
labs(color = NULL) +
customized_theme()
CB_POSITION <- c(0.8, 0.35)
p_embedding_timecourse_umi <- plot_embedding_value(
embedding = embedding_timecourse[, c(x_column, y_column)],
color_values = log10(Matrix::colSums(matrix_readcount_use[, embedding_timecourse$cell]) + 1),
colorbar_position = CB_POSITION,
label = paste0(EMBEDDING_TITLE_PREFIX, "; UMI"),
label_position = NULL,
geom_point_size = 0.6,
sort_values = TRUE,
FUN = function(x) x
)purrr::reduce(
list(
p_embedding_timecourse_cluster,
p_embedding_timecourse_batch,
p_embedding_timecourse_umi
), `+`
) +
patchwork::plot_layout(ncol = 3) +
patchwork::plot_annotation(
theme = theme(plot.margin = margin())
)
Polishing
p_embedding_timecourse_batch <- plot_embedding(
embedding = embedding_timecourse[, c(x_column, y_column)],
color_values = embedding_timecourse$batch %>% as.factor(),
# label = "t-SNE; Batch",
label_position = NULL,
show_color_value_labels = FALSE,
show_color_legend = FALSE,
geom_point_size = 0.4,
sort_values = FALSE
) +
scale_color_manual(
values = yarrr::piratepal(palette = "google") %>% as.character(),
labels = c("D3", "D6", "D9", "D9; PXGL")
) +
guides(colour = guide_legend(override.aes = list(size = 2), ncol = 1)) +
labs(color = NULL) +
customized_theme(legend_key_size = 1, legend_text_size = 5) +
guides(
colour = guide_legend(
ncol = 1,
override.aes = list(size = 2)
),
byrow = TRUE
)embedding_timecourse %<>%
dplyr::select(cell:y_fitsne) %>%
left_join(
embedding %>%
select(cell, louvain),
by = c("cell" = "cell")
) %>%
dplyr::rename(louvain = louvain.x, louvain_ = louvain.y) %>%
mutate(
louvain_ = case_when(
is.na(louvain_) ~ "NA",
TRUE ~ as.character(louvain_)
),
#
lineage = case_when(
louvain_ %in% c(11) ~ "EPI",
louvain_ %in% c(18) ~ "HYP",
louvain_ %in% c(0, 1, 8, 9, 12, 17) ~ "TE",
louvain_ %in% c(10, 14) ~ "Pre-lineage",
TRUE ~ "Other"
),
lineage = factor(
lineage,
levels = c("Other", "TE", "Pre-lineage", "HYP", "EPI")
),
#
cluters_selected = case_when(
louvain_ %in% c(2, 3, 4, 5, 6, 7, 13, 15, 16) ~ louvain_,
TRUE ~ "Other"
),
cluters_selected = factor(
cluters_selected,
levels = c("Other", "2", "3", "4", "5", "6", "7", "13", "15","16")
)
)p_embedding_timecourse_lineage <- embedding_timecourse %>%
filter(
lineage == "Other"
) %>%
ggplot(
aes(
x = x_fitsne,
y = y_fitsne
)
) +
geom_point(size = 0.6, stroke = 0, shape = 16, color = "grey70") +
geom_point(
data = embedding_timecourse %>% filter(lineage != "Other"),
aes(x = x_fitsne, y = y_fitsne, color = lineage),
size = 0.4, stroke = 0, shape = 16
) +
scale_color_manual(
name = NULL,
values = c("grey70", "#8ace7e", "#ff684c", "#9467bd", "#ffd8b1"),
breaks = c("Other", "EPI", "HYP", "TE", "Pre-lineage"),
labels = c("Other", "ELCs", "HLCs", "TLCs", "Pre-lineage-like")
) +
theme_void() +
customized_theme(legend_key_size = 1, legend_text_size = 5) +
guides(
colour = guide_legend(
ncol = 1,
override.aes = list(size = 2)
),
byrow = TRUE
)p_embedding_timecourse_cluster_matched <- embedding_timecourse %>%
filter(
cluters_selected == "Other"
) %>%
ggplot(aes(x = x_fitsne, y = y_fitsne)) +
geom_point(size = 0.6, stroke = 0, shape = 16, color = "grey70") +
geom_point(
data = embedding_timecourse %>% filter(cluters_selected != "Other"),
aes(x = x_fitsne, y = y_fitsne, color = cluters_selected),
size = 0.4, stroke = 0, shape = 16
) +
scale_color_manual(
name = NULL,
values = c("Other" = "grey70", color_palette_cluster)
) +
theme_void() +
customized_theme(legend_key_size = 1, legend_text_size = 5) +
guides(
colour = guide_legend(
ncol = 2,
override.aes = list(size = 2)
),
byrow = TRUE
) +
ggrepel::geom_text_repel(
data = get_middle_points(
embedding_timecourse %>% filter(cluters_selected != "Other"),
x_fitsne,
y_fitsne,
cluters_selected
),
ggplot2::aes(
x = x_fitsne,
y = y_fitsne,
label = cluters_selected
),
color = "black",
size = 1.8,
family = "Arial",
#
box.padding = 0.4,
point.padding = 1e-06,
min.segment.length = 0,
arrow = ggplot2::arrow(length = unit(0.015, "npc")),
max.overlaps = Inf,
nudge_x = 0,
nudge_y = 10,
#
segment.color = "grey35",
segment.size = 0.25,
segment.alpha = 1,
# segment.inflect = TRUE,
seed = 20201121
)## `summarise()` has ungrouped output. You can override using the `.groups` argument.## Joining, by = "cluters_selected"## Warning in min(distance): no non-missing arguments to min; returning Inf## Joining, by = "cluters_selected"
## Joining, by = "cluters_selected"
## Joining, by = "cluters_selected"
## Joining, by = "cluters_selected"
## Joining, by = "cluters_selected"
## Joining, by = "cluters_selected"
## Joining, by = "cluters_selected"
## Joining, by = "cluters_selected"
## Joining, by = "cluters_selected"## Warning: Ignoring unknown parameters: max.overlaps# vertical
purrr::reduce(
list(
p_embedding_timecourse_batch,
p_embedding_timecourse_lineage,
p_embedding_timecourse_cluster_matched
), `+`
) +
patchwork::plot_layout(ncol = 1) +
patchwork::plot_annotation(
theme = theme(plot.margin = margin())
)
Expression
CB_POSITION <- c(0.8, 0.36)
x_label <- ggplot_build(p_embedding_timecourse_batch)$layout$panel_params[[1]][c("x.range")] %>%
unlist() %>%
quantile(0.1)
y_label <- ggplot_build(p_embedding_timecourse_batch)$layout$panel_params[[1]][c("y.range")] %>%
unlist() %>%
quantile(0.875)
purrr::map(features_selected, function(x) {
selected_feature <- x
plot_embedding_value(
embedding = embedding_timecourse[, c(x_column, y_column)],
color_values = log10(
matrix_cpm_use[selected_feature, embedding_timecourse$cell] + 1
),
colorbar_position = CB_POSITION,
label = x %>% str_remove("^E.+_"),
label_position = c(x_label, y_label),
geom_point_size = 0.5,
sort_values = TRUE,
FUN = function(x) x
)
}) %>%
purrr::reduce(`+`) +
plot_layout(nrow = 1) +
plot_annotation(theme = theme(plot.margin = margin()))
Cluster composition
prepare_cluster_composition(
embedding = embedding_timecourse,
x = louvain,
group = batch
) %>%
mutate(
louvain = as.factor(louvain)
) %>%
plot_barplot(
x = louvain,
y = percentage,
z = batch
) +
scale_fill_manual(
values = yarrr::piratepal(palette = "google") %>% as.character(),
labels = c("D3", "D6", "D9", "D9; PXGL")
)
R session info
devtools::session_info()$platform## setting value
## version R version 4.0.3 (2020-10-10)
## os macOS 11.1
## system x86_64, darwin20.1.0
## ui unknown
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz America/Chicago
## date 2020-12-16devtools::session_info()$pack %>%
as_tibble() %>%
dplyr::select(
package,
loadedversion,
date,
`source`
) %>%
# print(n = nrow(.))
gt::gt() %>%
gt::tab_options(table.font.size = "median")| package | loadedversion | date | source |
|---|---|---|---|
| assertthat | 0.2.1 | 2019-03-21 | CRAN (R 4.0.0) |
| backports | 1.2.1 | 2020-12-09 | CRAN (R 4.0.3) |
| BayesFactor | 0.9.12-4.2 | 2018-05-19 | CRAN (R 4.0.3) |
| broom | 0.7.2.9000 | 2020-12-12 | Github (tidymodels/broom@810115d) |
| callr | 3.5.1.9000 | 2020-11-23 | Github (r-lib/callr@d44e660) |
| cellranger | 1.1.0 | 2016-07-27 | CRAN (R 4.0.0) |
| checkmate | 2.0.0 | 2020-02-06 | CRAN (R 4.0.0) |
| circlize | 0.4.11 | 2020-10-31 | CRAN (R 4.0.3) |
| cli | 2.2.0 | 2020-11-20 | CRAN (R 4.0.3) |
| coda | 0.19-4 | 2020-09-30 | CRAN (R 4.0.2) |
| codetools | 0.2-16 | 2018-12-24 | CRAN (R 4.0.3) |
| colorspace | 2.0-0 | 2020-11-10 | R-Forge (R 4.0.3) |
| crayon | 1.3.4 | 2017-09-16 | CRAN (R 4.0.0) |
| DBI | 1.1.0 | 2019-12-15 | CRAN (R 4.0.0) |
| dbplyr | 2.0.0 | 2020-11-03 | CRAN (R 4.0.3) |
| desc | 1.2.0 | 2018-05-01 | CRAN (R 4.0.0) |
| devtools | 2.3.1.9000 | 2020-12-05 | Github (r-lib/devtools@b3be019) |
| digest | 0.6.27 | 2020-10-24 | CRAN (R 4.0.3) |
| dplyr | 1.0.2.9000 | 2020-12-14 | Github (tidyverse/dplyr@59105eb) |
| ellipsis | 0.3.1 | 2020-05-15 | CRAN (R 4.0.3) |
| evaluate | 0.14 | 2019-05-28 | CRAN (R 4.0.0) |
| extrafont | 0.17 | 2014-12-08 | CRAN (R 4.0.2) |
| extrafontdb | 1.0 | 2012-06-11 | CRAN (R 4.0.0) |
| fansi | 0.4.1 | 2020-01-08 | CRAN (R 4.0.0) |
| farver | 2.0.3 | 2020-01-16 | CRAN (R 4.0.0) |
| forcats | 0.5.0.9000 | 2020-12-09 | Github (tidyverse/forcats@6c5e59f) |
| fs | 1.5.0 | 2020-07-31 | CRAN (R 4.0.3) |
| generics | 0.1.0 | 2020-10-31 | CRAN (R 4.0.3) |
| ggplot2 | 3.3.2.9000 | 2020-12-14 | Github (tidyverse/ggplot2@9deb97b) |
| GlobalOptions | 0.1.2 | 2020-06-10 | CRAN (R 4.0.0) |
| glue | 1.4.2 | 2020-08-27 | CRAN (R 4.0.2) |
| gt | 0.2.2 | 2020-11-25 | Github (rstudio/gt@bae32f4) |
| gtable | 0.3.0 | 2019-03-25 | CRAN (R 4.0.0) |
| gtools | 3.8.2 | 2020-03-31 | CRAN (R 4.0.0) |
| haven | 2.3.1 | 2020-06-01 | CRAN (R 4.0.0) |
| hms | 0.5.3 | 2020-01-08 | CRAN (R 4.0.0) |
| htmltools | 0.5.0.9003 | 2020-12-03 | Github (rstudio/htmltools@d18bd8e) |
| httr | 1.4.2 | 2020-07-20 | CRAN (R 4.0.2) |
| jpeg | 0.1-8.1 | 2019-10-24 | CRAN (R 4.0.0) |
| jsonlite | 1.7.2 | 2020-12-09 | CRAN (R 4.0.3) |
| knitr | 1.30.4 | 2020-12-15 | Github (yihui/knitr@f8f90ba) |
| labeling | 0.4.2 | 2020-10-20 | CRAN (R 4.0.3) |
| lattice | 0.20-41 | 2020-04-02 | CRAN (R 4.0.3) |
| lifecycle | 0.2.0 | 2020-03-06 | CRAN (R 4.0.0) |
| lubridate | 1.7.9.2 | 2020-12-12 | Github (tidyverse/lubridate@04be2a8) |
| magrittr | 2.0.1.9000 | 2020-12-14 | Github (tidyverse/magrittr@bb1c86a) |
| Matrix | 1.2-18 | 2019-11-27 | CRAN (R 4.0.2) |
| MatrixModels | 0.4-1 | 2015-08-22 | CRAN (R 4.0.0) |
| memoise | 1.1.0 | 2017-04-21 | CRAN (R 4.0.0) |
| modelr | 0.1.8.9000 | 2020-11-23 | Github (tidyverse/modelr@16168e0) |
| munsell | 0.5.0 | 2018-06-12 | CRAN (R 4.0.0) |
| mvtnorm | 1.1-1 | 2020-06-09 | CRAN (R 4.0.2) |
| patchwork | 1.1.0 | 2020-11-09 | CRAN (R 4.0.3) |
| pbapply | 1.4-3 | 2020-08-18 | CRAN (R 4.0.2) |
| pillar | 1.4.7 | 2020-11-20 | CRAN (R 4.0.3) |
| pkgbuild | 1.1.0 | 2020-07-13 | CRAN (R 4.0.3) |
| pkgconfig | 2.0.3 | 2019-09-22 | CRAN (R 4.0.0) |
| pkgload | 1.1.0 | 2020-05-29 | CRAN (R 4.0.0) |
| prettyunits | 1.1.1.9000 | 2020-11-23 | Github (r-lib/prettyunits@b1cdad8) |
| processx | 3.4.5 | 2020-11-30 | CRAN (R 4.0.3) |
| ps | 1.5.0 | 2020-12-05 | CRAN (R 4.0.3) |
| purrr | 0.3.4.9000 | 2020-11-23 | Github (tidyverse/purrr@af06d45) |
| R6 | 2.5.0 | 2020-11-02 | Github (r-lib/R6@6cf7d4e) |
| ragg | 1.0.0.9000 | 2020-12-16 | Github (r-lib/ragg@cd1a50f) |
| Rcpp | 1.0.5 | 2020-07-06 | CRAN (R 4.0.3) |
| readr | 1.4.0.9000 | 2020-11-23 | Github (tidyverse/readr@97186a8) |
| readxl | 1.3.1.9000 | 2020-11-23 | Github (tidyverse/readxl@9f85fa5) |
| remotes | 2.2.0.9000 | 2020-12-03 | Github (r-lib/remotes@5d0d9fd) |
| reprex | 0.3.0 | 2019-05-16 | CRAN (R 4.0.0) |
| reticulate | 1.18 | 2020-10-25 | CRAN (R 4.0.3) |
| rlang | 0.4.9.9000 | 2020-12-16 | Github (r-lib/rlang@b5f7d34) |
| rmarkdown | 2.6.0001 | 2020-12-15 | Github (rstudio/rmarkdown@80f14b2) |
| rprojroot | 2.0.2 | 2020-11-15 | CRAN (R 4.0.3) |
| rstudioapi | 0.13 | 2020-11-12 | CRAN (R 4.0.3) |
| Rttf2pt1 | 1.3.8 | 2020-01-10 | CRAN (R 4.0.0) |
| rvest | 0.3.6 | 2020-07-25 | CRAN (R 4.0.2) |
| scales | 1.1.1 | 2020-05-11 | CRAN (R 4.0.3) |
| sessioninfo | 1.1.1 | 2018-11-05 | CRAN (R 4.0.3) |
| shape | 1.4.5 | 2020-09-13 | CRAN (R 4.0.2) |
| stringi | 1.5.3 | 2020-09-09 | CRAN (R 4.0.2) |
| stringr | 1.4.0.9000 | 2020-11-23 | Github (tidyverse/stringr@1f03eb0) |
| styler | 1.3.2.9000 | 2020-11-30 | Github (r-lib/styler@d5b8b0e) |
| systemfonts | 0.3.2.9000 | 2020-12-16 | Github (r-lib/systemfonts@7df5e29) |
| testthat | 3.0.0.9000 | 2020-12-15 | Github (r-lib/testthat@6227640) |
| textshaping | 0.2.1.9000 | 2020-12-16 | Github (r-lib/textshaping@e71ce33) |
| tibble | 3.0.4.9000 | 2020-12-13 | Github (tidyverse/tibble@5881b43) |
| tidyr | 1.1.2.9000 | 2020-12-09 | Github (tidyverse/tidyr@581b488) |
| tidyselect | 1.1.0 | 2020-05-11 | CRAN (R 4.0.3) |
| tidyverse | 1.3.0.9000 | 2020-11-23 | Github (hadley/tidyverse@8a0bb99) |
| usethis | 2.0.0.9000 | 2020-12-13 | Github (r-lib/usethis@45d0fef) |
| vctrs | 0.3.5 | 2020-11-17 | CRAN (R 4.0.3) |
| viridisLite | 0.3.0 | 2018-02-01 | CRAN (R 4.0.0) |
| withr | 2.3.0 | 2020-09-22 | CRAN (R 4.0.2) |
| xfun | 0.19 | 2020-10-30 | CRAN (R 4.0.3) |
| xml2 | 1.3.2 | 2020-04-23 | CRAN (R 4.0.0) |
| yaml | 2.2.1 | 2020-02-01 | CRAN (R 4.0.0) |
| yarrr | 0.1.5 | 2017-04-19 | CRAN (R 4.0.3) |